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WaterSystems.jl is a Julia package for defining water delivery infrastructure system data and problem definitions. The WaterSystems.jl package enables parsing from the EPANET *.inp file format and the expression of data for WaterSimulations.jl models. WaterSystems.jl defines a data schema for water infrastructure networks.
The existing refrigeration system for OpenStudio was originally designed for a prototype supermarket model. To support the development of new modular space types, it is crucial to refactor the system. This process includes refining the design by utilizing the manufacturer's existing data, along with incorporating the latest data from manufacturers, to enhance the system’s adaptability and functionality. This repository currently contains the refrigeration system database and Python-based automation for generating OpenStudio-compatible refrigeration JSON files.
The on-road transportation of cryomodules (CM) is a critical phase during which the structure may be subject to relevant dynamic loading. Thus, an accurate design of Transportation Tool (TT), equipped with a proper suspension system, is mandatory. In this paper the TT design for the PIP-II proto SSR1 CM is presented. A finite element (FE) model was developed considering the main CM parts. However, the full model was not suited for the design of the suspension system because of its computational time. Thus, it was exported as a Modal Neutral File to a multibody (MB) software, where minor components were modeled as rigid bodies or lumped stiffnesses. The reduced MB model considerably shortened the computational time and it was exploited for the design of the TT, which includes helical isolators (HI) acting as a mechanical filter. A real 3D acceleration profile, acquired during the transportation of a LCLS-II CM from Fermilab to SLAC, was used to validate the TT effectiveness in reducing the vibrational loading. In addition, the results of the MB analysis were used to perform FE analysis of critical components, such as bellows.
We used entropy and wavelet techniques to identify temporal variability in geochemical constituents and their controls along three transects within a contaminated floodplain system near Rifle CO. The objectives of this study were to: (1) identify hot moments of conservative and redox sensitive species as short time-periods with disproportionally high levels of concentration, and (ii) associate the transport and/or biogeochemical factors controlling this temporal variability. This data package includes tables from Arora et al. 2016a: Identifying geochemical hot moments and their controls on a contaminated river floodplain system using wavelet and entropy approaches, Environmental modelling & software, 85, 27-41. In particular, files include sampling frequency for geochemical data at the Rifle site, and results of entropy analysis.
Land surface models such as Community Land Model Version 5 (CLM5) are essential tools for simulating the behaviors of the terrestrial system. Despite the extensive application of CLM5, limited attention has been paid to the underlying uncertainties associated with its hydrologic parameters and the implications that these uncertainties have on water resources applications. To address this long-standing issue, we conduct a comprehensive hydrologic parameter uncertainty characterization (UC) of CLM5 over the hydroclimatic gradients of the Conterminous United States using five meteorological datasets. Key datasets produced from the UC experiment include a benchmark dataset of CLM5 default hydrological performance, parameter sensitivity identified for 28 hydrological metrics, and large ensemble outputs for hydrological predictions. The presented datasets can assist CLM5 calibration and to support broad applications such as evaluating vulnerabilities to droughts and floods. The dataset can be used to identify under what hydroclimate conditions parametric uncertainties demonstrate substantial effects on hydrological predictions and clarify where further investigations are needed to understand how land runoff uncertainties interact with other Earth system processes. Please refer to the included README.pdf for a description of the included files. Note that raw CLM5 model outputs for each forcing dataset are hosted on a Globus endpoint: https://app.globus.org/file-manager?destination_id=d22ef858-27b0-11ed-a910-fd3165076336.
A method and system of publishing a map includes providing access to a plurality of map data files or mapping services between at least one publisher and at least one subscriber; defining a map in a map context comprising parameters and descriptors to substantially duplicate a map by reference to mutually accessible data or mapping services, publishing a map to a channel in a table file on server; accessing the channel by at least one subscriber, transmitting the mapping context from the server to the at least one subscriber, executing the map context by the at least one subscriber, and generating the map on a display software associated with the at least one subscriber by reconstituting the map from the references and other data in the mapping context.
Toolpath generation for extrusion-based additive manufacturing systems, called slicing, involves operations on polygonal contours that are derived from an STL file. Slicing generates multiple paths per layer (both closed-loop and open-loop) that are designed to optimally fill the space outlined by the polygon(s). In the course of printing a layer, the extruder must start and stop, the tip must be wiped, and the extruder must travel between paths without printing. Any amount of time the printer spends moving without printing is considered wasted time because the part isn’t being constructed. In addition, the start/stop point, known as the seam, is often a blemish on the surface of the part that contributes to weaker material properties. Therefore, a single path for creating multi-bead walled structures is desirable because it would save machine time and create parts with better surface finish. This paper will cover one method of modifying the CAD file and slicing engine to allow for parts to be printed without starting and stopping the extruder.
The advanced non-light water reactor designs (Gen IV reactors), including molten salt/ very high temperature/ sodium-cooled and lead-cooled fast reactors, typically operate at higher temperatures and more extreme radiation conditions than light water reactors. An intrinsic part of the deployment and progress of Gen IV reactor designs is selecting the most suitable structural material for a specific application. Additive manufacturing (AM), a fairly new process of making physical, three-dimensional objects from a computer design file, is going to completely change the way of design, build and certify nuclear systems. It offers a range of opportunities to produce complex geometries from existing materials, offers new routes for processing of previously difficult to process materials, allows for design of new high-performance materials, and finally facilitates hybridization of dissimilar materials. This emerging technology has successfully produced cars, wind turbine blade molds and even live cells. It could also open up big opportunities for the nuclear industry to quickly deploy technologies at a fraction of the cost. So far, AM techniques have been preliminarily applied in the field of nuclear reactors, including the classical parts such as the pressure vessel of a small reactor with 508-III steel, the bottom nozzle of a fuel assembly with 304L steel, the fuel cladding with zirconium alloy and the integrated impeller of a pump and the multi-channel valve body with 316L steel [6,7]. The AM applications for operating nuclear reactors started in auxiliary plant components and have slowly migrated to metallic reactors and core components, but many of these are not safety critical components. Although many parts used for nuclear reactors have been fabricated by AM techniques, practical applications in engineering are still a long way off due to the uncertainty factors focused on the processing, material properties, analysis methods and application standards, which feeds the safety and life-cycle of the nuclear reactor. Due to rapid, repeated heating and cooling during production, a high dislocation density was present in the AM material. This microstructure feature is unstable at elevated temperature while high temperature is one of the typical operation environments for nuclear reactors. Thus, it is important to understand the thermal effect on the microstructure of AM material. The objectives of this study are to investigate the effect of heat treatment on the microstructure and mechanical properties of 316L stainless steel produced by laser powder bed fusion additive manufacturing, and to determine an appropriate heat treatment practice that will be applied to the lightweight AM lattice-structured material with the same chemistry. The heat treatment study consisted of annealing the samples at a temperature range of 800 to 1200 oC with a 50 oC increment for different times (1-24 hours), followed by vacuum or air cooling. Microstructural characterization was carried out by Scanning Electron Microscope (SEM). Grain size and crystallographic orientation were investigated by Electron Backscatter Diffraction (EBSD). Vickers hardness tests with a 0.5 kg load were employed to determine the hardness of samples after different heat treatments. After heat treatment, the random crystallographic orientation was preserved, and the volume fraction of high-angle grain boundaries (grain boundary misorientation =15 oC) remained the same. The dislocation density decreased with annealing temperature due to recovery. The fine subgrain structures in the as-printed specimen were quite stable up to 1200 oC. Minimal recrystallization was observed up to 1200 oC. Recrystallization initiated only after 8.5 hours at 1200 oC. The SEM images did not show obvious dependence of microstructure on cooling rate. The hardness of the specimens decreased with increasing annealing temperature as a result of the decrease in dislocation density. It is interesting to note that the AM material showed very similar hardness to the wrought material when annealing at similar temperature, although the microstructures are very different. Annealing at 1050 oC for 1 hour followed by air cooling was selected as the heat treatment procedure for the lattice designed lightweight AM 316L material.
As part of the conversion of the MCNP®testing system to CTest, a tool was needed to generate CMake variables from JSON formatted data structure. The system was first implemented using the JSON to CMake parser which with some slight modifications provided data useful for the MCNP CTest testing system. Upon further evaluation, we found it to be slow when setting up the 1600 tests during the CMake configuration step. Not only was it slow, it did not validate the JSON files during configuration allowing for unexpected behavior. Upon some web searching we came across a simple recursive python example that would provide the same capability needed by our testing system. This python example was effectively wrapped into a single python script that was used to convert the JSON data into CMake variables. This tool is attached as json_to_cmake.py and is listed in the appendix. In essence, the tool provides single variables for each value defined within the JSON file. The name for the variable is constructed by using the associated object names and array indicies needed to deference the variable itself. In the following sections, examples are provided to better describe how this is done.
This dataset provides field site documentation for related datasets including GPS waypoints for intensive plots, reference points, vegetation plots, and soil temperature/moisture monitoring stations that were established in July 2016 at the Kougarok hill slope located at Kougarok Road, Mile Marker 64. At all intensive plots and reference points, photographs of the surrounding area were also taken using an Olympus Stylus TG-3 digital camera. Photographs were July 2nd through July 7th looking in each of the four cardinal directions. GPS waypoints were taken using a Garmin eTrek 10 handheld GPS and World Geodetic System 1984 (WGS-84). The error associated with the Garmin GPS coordinates was 9 foot or approximately 3 m. When marking waypoints, users waited until the instrument reached this error level before recording. Coordinate units are decimal degrees. This dataset is comprised of one comma-separated (*.csv) file containing GPS waypoints locating intensive plots, reference points, soil temperature/moisture monitoring stations, and vegetation plots. The dataset also contains one folder of photographs (*.jpg) taken at intensive stations, one folder of photographs (*.jpg) taken at reference points, and one User file (*.pdf). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
This report documents fiscal year 2026 enhancements to the System Analysis Module (SAM) for modeling fission product transport in liquid-fueled molten salt reactors (MSRs). The work advances three principal areas: noble gas transport, noble metal deposition, and user interface improvements. The noble gas transport capability integrates drift-flux gas transport, Henry’s law two-film interphase mass transfer with pressure-based nucleation suppression, Knudsen-regime pore diffusion into porous graphite with a conjugate salt-graphite interface constraint, built-in material properties, five Sherwood-number mass transfer correlations including three derived from high-fidelity NekRS simulations, and xenon-135 reactivity feedback through SAM’s point-kinetics model. This work also presents a comprehensive verification test suite, including new analytically verified cases for pressure-dependent onset of interphase gas transfer in a stagnant vertical pipe, a postulated FLiBe-graphite Xe extraction permeator, a gravity riser with a fission-product source, and a descending pipe with gas redissolution driven by hydrostatic pressure. A machine learning framework for bubble rise velocity prediction in molten salt systems is developed and benchmarked on molten-salt and diverse aqueous bubble datasets. The best-performing fine-tuned transfer-learning networks achieve an 82% reduction in RMSE relative to the Clift correlation, and is implemented directly in SAM. The noble metal transport capability is developed, including a liquid-wall deposition model and a gas-surface flotation mechanism that transfers insoluble particles entrained by sparging gas to wetted structures. Verification tests and demonstration cases cover the surface deposition, flotation efflux, and flotation shedding. Finally, a new [SpeciesTransport] input structure replaces positional global vectors with selfcontained, order-independent, named species blocks, simplifies the specification of multiphase species and decay chains, and remains fully compatible with existing SAM input files. Together, these developments improve the physical fidelity, verification basis, and usability of SAM for system-level analyses of fissionproduct behavior in MSRs.
Lawrence Berkeley National Laboratory (Berkeley Lab) estimates hourly project-level generation data for utility-scale solar projects and hourly county-level generation data for residential and non-residential distributed photovoltaic (PV) systems in the seven organized wholesale markets and 10 additional Balancing Areas. To encourage its broader use, Berkeley Lab has made this data file public here at OEDI, covering the years 2012-2020. The public project-level dataset is updated annually with data from the previous calendar year. For more information about the research project, including a technical report, briefing material, visualizations, and additional data, please visit the project homepage linked in this submission.
The Python-based program is a laboratory automation tool designed to control and monitor electrochemical systems. The tool was developed for capacitive deionization (CDI) experiments, but it can be used for any system that requires controlled voltage or current segments and multi-parameter monitoring. The program integrates hardware components to run user-defined experimental parameters, providing operational control of a programmable power supply, peristaltic pump, and data acquisition devices. Currently, the program is structured with a workflow that includes an initialization (or pre-run) phase, a main loop, and a post-experiment stabilization (or post-run) phase. The initialization phase prepares and stabilizes the cell, ensuring that the electrodes and solution reach a baseline state before the experiment begins. The main loop consists of multiple voltage segments that repeat, controlling the experiment while recording key parameters such as time, voltage, current, pH, and conductivity. Finally, the post-experiment stabilization phase allows the system to stabilize after the experiment, returning the cell and solution to equilibrium conditions before ending the sequence. The program is designed with four variations, each tailored to different experimental needs. All variations include both the initialization and post-experiment stabilization stages, which run for a set amount of time, voltage, current, and flow rate before and after the main experiment block. The main loop runs for a set number of cycles, as defined by the user input, and each cycle is composed of 2 or 4 segments. The 4 program variations are described as follows: Program 1: The main program includes 2 segments. Each segment is defined to have a set duration, flow rate, voltage, and current. This program measures conductivity, flow rate, voltage, and current. Program 2: The main program expands Program 1 to include 4 segments. Each segment has a specified duration, flow rate, voltage, and current. Like Program 1, it measures conductivity, flow rate, voltage, and current. Program 3: The main program consists of 2 segments, each defined by time, flow rate, voltage, and current. In addition to conductivity, flow rate, voltage, and current, Program 3 collects pH and temperature data through a 4-channel data acquisition device. Program 4: This program independently controls two channels of a multi-channel power supply simultaneously. While conductivity can only be measured for one cell at a time, the dual-channel control makes it possible to operate two cells simultaneously under different voltage/current conditions. The main program includes 2 segments.For each program, all measurements are automatically logged and integrated into a single Excel output file. Data are displayed in numerical format and plotted, both in real time, to track system performance. A key feature of the program is its ability to synchronize all outputs so that every measurement shares a single timestamp, ensuring accurate alignment of voltage, current, pH, conductivity, and pH data.By combining hardware control, real-time monitoring, and unified data collection, this program significantly reduces manual workload and minimizes errors, making it a reliable platform for researchers, engineers, and laboratory technicians conducting CDI experiments, among other electrochemical tests.
Recent efforts at Sandia such as DataSEA are creating search engines that enable analysts to query the institution’s massive archive of simulation and experiment data. The benefit of this work is that analysts will be able to retrieve all historical information about a system component that the institution has amassed over the years and make better-informed decisions in current work. As DataSEA gains momentum, it faces multiple technical challenges relating to capacity storage. From a raw capacity perspective, data producers will rapidly overwhelm the system with massive amounts of data. From an accessibility perspective, analysts will expect to be able to retrieve any portion of the bulk data, from any system on the enterprise network. Sandia’s Institutional Computing is mitigating storage problems at the enterprise level by procuring new capacity storage systems that can be accessed from anywhere on the enterprise network. These systems use the simple storage service, or S3, API for data transfers. While S3 uses objects instead of files, users can access it from their desktops or Sandia’s high-performance computing (HPC) platforms. S3 is particularly well suited for bulk storage in DataSEA, as datasets can be decomposed into object that can be referenced and retrieved individually, as needed by an analyst. In this report we describe our experiences working with S3 storage and provide information about how developers can leverage Sandia’s current systems. We present performance results from two sets of experiments. First, we measure S3 throughput when exchanging data between four different HPC platforms and two different enterprise S3 storage systems on the Sandia Restricted Network (SRN). Second, we measure the performance of S3 when communicating with a custom-built Ceph storage system that was constructed from HPC components. Overall, while S3 storage is significantly slower than traditional HPC storage, it provides significant accessibility benefits that will be valuable for archiving and exploiting historical data. There are multiple opportunities that arise from this work, including enhancing DataSEA to leverage S3 for bulk storage and adding native S3 support to Sandia’s IOSS library.
Traditional filesystems organize data in directories. These directories are typically a collection of files whose grouping is based on a single criterion, e.g., the starting date of an experiment, experiment name, beamline ID, measurement device, or instrument. However, each file in a directory can belong to several logical groups, such as a special event type, experiment condition, or a part of a selected dataset. dCache is a storage system developed to store large amounts of scientific data, used by many HEP and Photon Science experiments. With recent developments in dCache, we have introduced a concept of file tagging, which dynamically groups files with the same label into virtual directories. The file labels can be added, removed, renamed, and deleted through the admin interface or via REST API. The files in virtual directories are exposed through all protocols supported by dCache. This contribution will describe the details of the implementation for file tagging in dCache and present our future development plans on automatic metadata extractions, a feature that will significantly simplify data management. Additionally, we are exploring the future use of virtual directories as a way to translate scientific data catalogs into filesystem views for direct data analysis.
Purpose: To investigate the effect of the trajectory files from linear accelerator for Clarkson-based independent dose verification in IMRT and VMAT plans. Methods: A CT-based independent dose verification software (Simple MU Analysis: SMU, Triangle Products, Japan) with a Clarksonbased algorithm was modified to calculate dose using the trajectory log files. Eclipse with the three techniques of step and shoot (SS), sliding window (SW) and Rapid Arc (RA) was used as treatment planning system (TPS). In this study, clinically approved IMRT and VMAT plans for prostate and head and neck (HN) at two institutions were retrospectively analyzed to assess the dose deviation between DICOM-RT plan (PL) and trajectory log file (TJ). An additional analysis was performed to evaluate MLC error detection capability of SMU when the trajectory log files was modified by adding systematic errors (0.2, 0.5, 1.0 mm) and random errors (5, 10, 30 mm) to actual MLC position. Results: The dose deviations for prostate and HN in the two sites were 0.0% and 0.0% in SS, 0.1±0.0%, 0.1±0.1% in SW and 0.6±0.5%, 0.7±0.9% in RA, respectively. The MLC error detection capability shows the plans for HN IMRT were the most sensitive and 0.2 mm of systematic error affected 0.7% dose deviation on average. Effect of the MLC random error did not affect dose error. Conclusion: The use of trajectory log files including actual information of MLC location, gantry angle, etc should be more effective for an independent verification. The tolerance level for the secondary check using the trajectory file may be similar to that of the verification using DICOM-RT plan file. From the view of the resolution of MLC positional error detection, the secondary check could detect the MLC position error corresponding to the treatment sites and techniques. This research is partially supported by Japan Agency for Medical Research and Development (AMED)
Abstract This paper describes development of a data acquisition system used to capture a range of occupancy related modalities from single-family residences, along with the dataset that was generated. The publicly available dataset includes: grayscale images at 32-by-32 pixels, captured every second; audio files, which have undergone processing to remove personally identifiable information; indoor environmental readings, captured every ten seconds; and ground truth binary occupancy status. The data acquisition system, coined the mobile human presence detection (HPDmobile) system, was deployed in six homes for a minimum duration of one month each, and captured all modalities from at least four different locations concurrently inside each home. The environmental modalities are available as captured, but to preserve the privacy and identity of the occupants, images were downsized and audio files went through a series of processing steps, as described in this paper. This dataset adds to a very small body of existing data, with applications to energy efficiency and indoor environmental quality.
SAND2025-11727O SetEnvironment software is a utility library module that uses ConfigParserEnhanced, also a library module, to read specifically formatted .ini files for setting the user's environment. It creates a consistent environment with an expected set of variables and module loads, which is important for testing. Environment variable operations include set, append, prepend, unset, remove, and others and uses the Python os.environ methods. The module operations are performed with a wrapper. Systems using the Lmod package can generate a module function, which can also perform module operations specified in the text file. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.